Fahad Saeed

Full Professor of Computing and Lab Director
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Short Bio

Dr. Fahad Saeed is Full Professor and Director of Graduate Studies in the Knight Foundation School of Computing and Information Sciences at Florida International University (FIU), Miami FL. He received his PhD in the Department of Electrical and Computer Engineering, University of Illinois at Chicago (UIC) in 2010. He was trained as a Post-Doctoral Fellow and Research Fellow in the Systems Biology Center at National Institutes of Health (NIH), Bethesda MD from Aug 2010 to January 2014 respectively, under the supervision of Mark Knepper. Prior to joining FIU, Prof. Saeed was a tenure-track Assistant Professor in the Department of Electrical & Computer Engineering and Department of Computer Science at Western Michigan University (WMU), Kalamazoo Michigan since Jan 2014. He was tenured and promoted to the rank of Associate Professor at WMU in August 2018. He has served as a visiting scientist in world-renowned prestigious institutions such as Department of Bio-Systems Science and Engineering (D-BSSE), ETH Zurich, Swiss Institute of Bioinformatics (SIB) and Epithelial Systems Biology Laboratory (ESBL) at National Institutes of Health (NIH) Bethesda, Maryland.

Dr. Saeed’s research interests are at the intersection of machine-learning, high performance computing and real-world applications, especially in computational biology. He is the director of Precision Computational Health and Biomedical Data Science Lab (Saeed Lab) at FIU. His lab develops machine-learning models, combined with high-performance computing, and data science approaches, to study the functional genomics (proteomics), and organization of the human brain and its function in the context of prediction, diagnosis and characterization of biomarkers specific to disorders such as epilepsy, ADHD, Autism, and Alzheimer’s. His research has been funded by NVIDIA, Intel/Altera, Xilinx, National Science Foundation (NSF) and National Institutes of Health (NIH) including the highly prestigious NSF CAREER, and NIH R01 (and R01-equivalent R35 MIRA) grants. More information about his lab research activities can be found at https://pcdslab.github.io/. He also maintains a webpage at https://prof-s.github.io

Complete list of publications is available at: https://scholar.google.com/citations?user=IPXv-GQAAAAJ&hl=en

Honors and Awards

  1. Excellence in Research and Creative Activities Award, Knight Foundation School of Computing and Information Science (KFSCIS), FIU, Dec 2024
  2. FIU Top Scholar, Research and Creative Activities , Florida International University, Sept 2022 CEC News Page
  3. Keynote Speaker at the 14th International Conference on Bioinformatics and Computational Biology (BICOB). More info here: BiCOB-2022 KeyNote Certificate
  4. Excellence in Applied Research Award, School of Computing and Information Science (SCIS), Florida International University (FIU), Dec 2020
  5. Distinguished Research and Creative Scholarship Award, Office of Vice President of Research WMU, Feb 2018
  6. NSF CAREER Award, 2017-2022
  7. ACM Senior Member, May 2017
  8. Outstanding New Researcher Award, College of Engineering and Applied Science (CEAS), Western Michigan University, Jan 2016 (1 faculty member gets the award in a single year for the entire college consisting of 7 academic departments)
  9. IEEE Senior Member, March 2015
  10. NSF CISE Research Initiation Initiative (CRII) Award, Feb 2015 - Feb 2018
  11. Fellows Award for Research Excellence (FARE), National Institutes of Health (NIH), June 2012 (Official award ceremony and US\$1000 travel grant)
  12. Travel award from Swiss Institute of Bioinformatics (SIB), Summers 2009.
  13. Recipient of Think Swiss Scholarship, by the Government of Switzerland for two years (2007 and 2008).
  14. Travel award from D-BSSE ETH Zurich, Summers 2008.

RESEARCH AND EDUCATIONAL INTERESTS

Machine-Learning for health and biomedical data, proteomics, neuroinformatics, computational systems biology, high-performance computing

EDUCATION AND PROFESSIONAL PREPARATION

Research Fellowship, Computational Systems Biology, National Institutes of Health, Bethesda MD. (2011-2014)

Postdoctoral Training, Computational Proteomics, National Institutes of Health, Bethesda MD. (2010-2011)

PhD, Electrical and Computer Engineering, University of Illinois at Chicago, Chicago IL USA. (2006-2010)

BSc Engg, Electrical Engineering, University of Engineering and Technology, Lahore. (2002-2006)

External Research Funds

Prof. Saeed has been awarded over US$ 7.43 million in external research funds - with more than US$ 6.05 million as a PI since 2014 (approx. US$ 700k per year). Most of the external research funds are competitively awarded from Federal Agencies such as National Science Foundation (NSF) and National Institutes of Health (NIH). Intramural funds and computing allocations are not included in this amount.

  1. National Science Foundation (NSF) IIS-2530255 [US$ 600,000], “Collaborative Research: CISE-ANR:III: Small: Leveraging External Data for Enhanced Understanding and Causal Attribution of Anomalies in Wastewater Networks”, Fahad Saeed (PI), with Kamal Premaratne (univ. Of Miami) and Helena Solo-Gabriele (Univ. of Miami), Benferhat Salem (Artois University, France), DELENNE Carole (Université de Montpellier France), and Nanee Chahinian (Aix Marseille University (Polytech Marseille/IUSTI)), Feb 2026 - Jan 2029 (combined funding of US$1.2 Million with 600k from French ANR, and 600k from NSF) NSF Award Page
  2. National Institutes of Health (NIH) R35GM153434 [US$ 1.75 million], “Machine­ Learning Models for big data omics”, Fahad Saeed (PI), June 2024 ­- June 2029 (single PI grant: MIRA R35 Outstanding Investigator mechanism) NIH Award Page
  3. National Science Foundation (NSF) OAC-2312599 [US$ 600,000], ``OAC Core: High Performance Computing Algorithms and Software for large-scale Mass Spectrometry based Omics”, Fahad Saeed (PI), Sept 2023 - Aug 2026 NSF Award Page

  4. National Science Foundation (NSF) TI-2322346 [US$ 275,000], ``STTR Phase I: Patient-Specific System for Early Detection and Identification of Epileptic Seizures “, Saba Mehmood (PI), Fahad Saeed (Co-PI), Oct 2023 - Sept 2024 NSF Award Page

  5. National Science Foundation (NSF) CHE-2304837 [US$ 500,000], ``Development of Multidimensional Ion Mobility-Tandem Mass Spectrometry (IMSn-FT-ICR MSn) Tools for the Characterization of Complex Mixtures”, Francisco Lima (PI), Fahad Saeed (Co-PI), Sept 2023 - August 2026 NSF Award Page
  6. National Science Foundation (NSF) TI-2213951 [US$ 250,000], ``PFI-TT: Artificial Intelligence-enabled Real-time System for Early Epileptic Seizure Detection and Prediction”, Fahad Saeed (PI), August 2022 - July 2024 NSF Award Page
  7. National Science Foundation (NSF) IIP-2143515 [US$ 50,000], ``I-Corps: Utilizing Machine learning and Artificial Intelligence (AI) for Early Detection and Identification of Mental Disorders”, Fahad Saeed (PI), Sept 2021 - Sept 2022 NSF Award Page
  8. National Institutes of Health (NIH) Supplemental- 3R01GM134384-02S1[US$ 100,000],”Compute-Cluster for Deep-Learning Models for Mass Spectrometry based Proteomics data” Fahad Saeed (PI), August 2021 - May 2023 NSF Award Page
  9. National Science Foundation (NSF) OAC-2126253 [US$ 400,000], ``CC* Compute: RAPTOR - Reconfigurable Advanced Platform for Transdisciplinary Open Research”, Jason Liu (PI), Jayantha Obeysekera (Co-PI), Keqi Zhang (Co-PI), Cassian D’Cunha (Co-PI), Mike Kirgan (SI), Yuepeng Li (SI), Vasilka Chergarova (SI), Yagya Joshi (SI), Jonathan Casco (SI), and Fahad Saeed (Co-PI), Sept 2021 - Sept 2023 NSF Award Page
  10. National Institutes of Health (NIH) Supplemental- 3R01GM134384-01A1S1 [US$ 205,291],”Multimodal Machine-Learning Algorithms for Early Detection, and Classification for Alzheimer Disorder and Related Dementia’s” Fahad Saeed (PI), May 2021 - May 2023 NIH Award Page
  11. National Institutes of Health (NIH), [R01GM134384] [US$ 965,874], “Multimodal Machine-Learning and High Performance Computing Strategies for Big MS Proteomics Data”, Fahad Saeed (PI) with Shu-Ching Chen (Co-Investigator), Jason Liu (Co-Investigator), Francisco Alberto Fernandez-Lima (Co-Investigator), and Sitharama Iyengar (Senior Personal), June 2020 - May 2023 NIH Award Page
  12. National Science Foundation (NSF) OAC-1925960 [US$ 415,950], “CAREER: Towards Fast and Scalable Algorithms for Big Proteogenomics Data Analytics” Fahad Saeed (PI), Sept 2018-April 2023 NSF Award Page
  13. National Science Foundation (NSF) CCF-1855441 [US$ 7,708], “CRII: SHF: HPC Solutions to Big NGS Data Compression”, Fahad Saeed (PI), Sept 2018 - Jan 31, 2020 NSF Award Page
  14. National Science Foundation (NSF) ACI-1651724 [US$ 499,999], “CAREER: Towards Fast and Scalable Algorithms for Big Proteogenomics Data Analytics” Fahad Saeed (PI), April 2017-April 2019 NSF Award Page
  15. National Institutes of Health (NIH) R15GM120820 [US$ 418,533], “Parallel Algorithms for Big Data from Mass Spectrometry based Proteomics” Fahad Saeed (PI), April 2017 - April 2020 NIH Award Page (change of PI when Dr. Saeed changed institution to FIU)
  16. National Science Foundation (NSF) REU Supplement [US$ 16,000], “CRII: SHF: HPC Solutions to Big NGS Data Compression” Fahad Saeed (PI), Feb 2016 - Feb 2018 NSF Award Page
  17. National Science Foundation (NSF) CCF-1464268 [US$ 171,341], “CRII: SHF: HPC Solutions to Big NGS Data Compression” Fahad Saeed (PI), (Feb 2015 - Feb 2018 NSF Award Page
  18. National Science Foundation (NSF) CNS-1250264 [US$ 200,000], “EAGER: High Performance Algorithms and Implementations for Biological Sequence Analysis and Genome Alignment” Ashfaq Khokhar, Fahad Saeed (Co-PI) (Sept 2012 - Aug 2015) NSF Award Page

Equipment and Computing Allocations

  1. Xilinx [US$ 13,195]}, ``Design and development of FPGA based MS omics pipeline”, Fahad Saeed(PI) (Equipment Grant, Versal AI Core EK-VCK190-G FPGA), March 2022
  2. NSF XSEDE Extended Collaborative Support Service (ECSS) [US$ 50,000,], ``DeepSNAP: Scalable Machine Learning for Mass Spectrometry based Proteomics”, Fahad Saeed (PI), (Jan 2021 - Dec 2021)
  3. National Science Foundation XSEDE ASC200004 [125,000.0 Service Units (SU)/10,000.0 GB SDSC Medium-term disk storage (Data Oasis)/10,000.0 GPU Hours/75,000 Core hours on Clusters: US$ 43,161.42], ``DeepSNAP: Scalable Machine Learning for Mass Spectrometry based Proteomics”, Fahad Saeed (PI), (Jan 2021 - Dec 2021)
  4. National Science Foundation XSEDE ASC200004 [100,000.0 Service Units (SU)/10,000.0 GB SDSC Medium-term disk storage (Data Oasis)/5,000.0 GPU Hours: US$ 6,190], “DeepSNAP: Scalable Machine Learning for Mass Spectrometry based Proteomics”, Fahad Saeed (PI), (March 2020 - Sept 2020)
  5. Intel Altera [US$ 7,900], “MS proteomics analysis using reconfigurable hardware”, Fahad Saeed(PI) (Equipment Grant, DE10-PRO-SX FPGA), Nov 2019
  6. National Science Foundation XSEDE supplemental grant TG-CCR150017 [30,000 Service Units (SU)/6TB SDSC Disk Storage/2500 GPU Hours: US$ 450], “Smart Index and Search for De Novo Proteogenomics”, Fahad Saeed (PI), (March 2019 - June 2020)
  7. National Science Foundation XSEDE renewal grant TG-CCR150017 [30,000 Service Units (SU)/6TB SDSC Disk Storage/2500 GPU Hours: US$ 3,159], “Smart Index and Search for De Novo Proteogenomics”, Fahad Saeed (PI), (March 2019 - March 2020)
  8. NVIDIA [US$ 1149], “High Performance Algorithms for Big Data Proteomics” Fahad Saeed (PI) (Equipment Grant for NVIDIA TITAN Xp GPU), August 2018
  9. National Science Foundation XSEDE renewal grant TG-CCR150017 [30,000 Service Units (SU)/6TB SDSC Disk Storage: US$ 6564], “A Distributed-Shared Memory Strategy to Speedup the Compression of Big Next-Generation Sequencing Datasets” Fahad Saeed (PI), (June 2016 - June 2018)
  10. National Science Foundation XSEDE startup grant TG-CCR150017 [30,000 Service Units (SU)], “Scalability study of compression algorithms for peta scale NGS data” Fahad Saeed (PI), (June 2015 - June 2016)
  11. Intel Altera [US$ 16,000], “Short Reads mapping to the genome using reconfigurable hardware” Fahad Saeed(PI) (Equipment Grant, 2 DE5-NET-450 FPGA’s), April 2014
  12. NVIDIA [US$ 5499], “High Performance Algorithms for Genome Alignments” Fahad Saeed (PI) (Equipment Grant for Tesla K40 GPU), Feb 2014

Intramural Grants

  1. Office of Vice President of Research,Western Michigan University (WMU) [US$ 129,570], “Scalable Algorithms for Big Proteogenomics Data Analytics” Fahad Saeed (PI), April 2017 - April 2020
  2. College and Engineering and Applied Science (CEAS), Western Michigan University (WMU) [US$ 41,650], “Developing HPC solutions to big fMRI data” Fahad Saeed (PI), April 2017 - June 2018</span></p>

Recent Media Coverage

  1. "Wearable devices could help predict seizures", FIU News, August 2022 Article Link
  2. "NIH Awards FIU $1M to Develop Machine Learning Algorithms to Study Proteins", HPC Wire, June 2020 Article Link
  3. "FIU researchers think of a way to speed up a vaccine for COVID-19", Miami Herald, June 2020 Article Link
  4. "Research Grant Helps FIU Professor Reach One Step Closer To Coronavirus Vaccine", FIU PantherNow, July 2020 Article Link
  5. "NIH awards FIU $1M to develop machine-learning algorithms to study proteins - important for understanding, treating diseases", FIU News, June 2020 Article Link
  6. "Dr. Saeed awarded prestigious $1 Million R01 grant from National Institute of Health", SCIS FIU News, May 2020 Article Link
  7. Distinguished Research and Creative Scholarship Award: Fahad Saeed",OVPR WMU, March 2018 Description YouTube
  8. Algorithms for Life, WMU Annual Magazine, Summer 2017
  9. Rising Stars in Research, WMU Annual Magazine, Summer 2017
  10. Researchers Snag a prestigious National Prize, WMU News Link, 2017
  11. WMU Assistant Professor receives NSF CAREER Award, CEAS WMU Article Link
  12. Surfing with Algorithms, NSF Science Node, March 2016
  13. Working with Powerful Supercomputers, WMU CEAS News, July 2015
Research

[dataset] MS-MLBenchmark: Comprehensive ML-ready Benchmark for Mass Spectrometry-based Proteomics

[method-development] CAVIAR: Cardiac and Advanced Vascular Intelligence Analytics & Research

[Method Development] Molecular and Protein Representation Learning

[Method Development] Predicting and Characterizing Alzheimer's Disease & Related Dementias

[analysis] Compressive and reductive analysis of genomic and proteomics data

[method-development] HPC Engine for Mass Spectrometry based Omics Data

[dataset] MLSPred-Bench: Reference EEG Benchmark for Prediction of Epileptic Seizures

[Method Development] Predicting Epileptic Seizures

[Method Development] ML Ecosystem for Mass Spectrometry Data

[Method Development] Characterization and diagnosis of Autism Spectrum

Papers

  1. ProtEnrich: Residual Multimodal Enrichment of Protein Sequence Embeddings

  2. CViT-ESP: Lightweight Pre-trained Vision Transformers for EEG-based Epileptic Seizure Prediction

  3. BindScreen: Protein-Centric Contrastive Learning for Sequence-Based Virtual Screening

  4. TITAN-BBB: Predicting BBB Permeability using Multi-Modal Deep-Learning Models

  5. MolDeBERTa: Foundational Model for Physicochemical and Structural-Informed Molecular Representation Learning

  6. Habenula alterations in resting state functional connectivity among autistic individuals

  7. Predicting progression of Alzheimer’s disease using blood-based multi-omics data

  8. End-to-end deep attention-based multitask pipeline for predicting uncertainty-quantified peptide properties from mass spectrometry data

  9. A Machine Learning and Benchmarking Approach for Molecular Formula Assignment of Ultra High-Resolution Mass Spectrometry Data from Complex Mixtures

  10. FiCOPS: Hardware and Software Co-Design of FPGA Computational Framework for Mass Spectrometry-Based Peptide Database Search

  11. Systems and methods for patient-specific epileptic seizure prediction

  12. fairGNN-WOD: fair graph learning without demographics

  13. RAPTOR: Reconfigurable Advanced Platform for Trans- disciplinary Open Research

  14. Overcoming Site Variability in Multisite fMRI Studies: An Autoencoder Framework for Enhanced Generalizability of Machine Learning Models

  15. MLSPred-Bench: Transforming Electroencephalography (EEG) Datasets into Machine Learning-Ready Seizure Prediction Benchmarks

  16. Machine-learning models for Alzheimer’s disease diagnosis using neuroimaging data: survey, reproducibility, and generalizability evaluation

  17. TA‐RNN: an Attention‐based Time‐aware Recurrent Neural Network Architecture to Predict Progression of Alzheimer’s Disease

  18. Alzheimer’s disease-associated gene ranking using PhenoGeneRanker

  19. Alzheimer’s disease diagnosis using gray matter of T1-weighted sMRI data and vision transformer

  20. Predicting Individual’s Cognitive Performance Through Multi-Omics Blood Data Using Hierarchical Input Neural Network - HINN

  21. Robustness of ML-Based Seizure Prediction Using Noisy EEG Data From Limited Channels

  22. Lightweight Transformer exhibits comparable performance to LLMs for Seizure Prediction: A case for light-weight models for EEG data

  23. Utilizing Pretrained Vision Transfomers and Large Language Models for Epileptic Seizure Prediction

  24. PVTAD: Alzheimer’s Disease Diagnosis Using Pyramid Vision Transformer Applied to White Matter of T1-Weighted Structural MRI Data

  25. Making MS Omics Data ML-Ready: SpeCollate Protocols

  26. Heterogeneity Aware Distributed Machine Learning at the Wireless Edge for Health IoT Applications: An EEG Data Case Study

  27. Communication Evaluation of a Wireless 4-Channel Wearable EEG for Brain-Computer Interface (BCI) and Healthcare Applications

  28. Systems and methods for matching mass spectrometry data with a peptide database

  29. Statistical and Machine Learning Analysis of the Human Brain Functional Network in a Multi-Site Resting-State Functional MRI Database Framework

  30. Q-CASA Invited Speakers Quantum-Centric Supercomputing Strategies for Neuroscience problems: Challenges and Progress

  31. PPAD: a deep learning architecture to predict progression of Alzheimer’s disease

  32. High Performance Computing Algorithms for Accelerating Peptide Identification from Mass-Spectrometry Data Using Heterogeneous Supercomputers

  33. GPU-acceleration of the distributed-memory database peptide search of mass spectrometry data

  34. Energy Efficient AI/ML based Continuous Monitoring at the Edge: ECG and EEG Case Study

  35. Description of Dissolved Organic Matter Transformational Networks at the Molecular Level

  36. Confounding Effects on the Performance of Machine Learning Analysis of Static Functional Connectivity Computed from rs-fMRI Multi-site Data

  37. ASD-GResTM: Deep Learning Framework for ASD classification using Gramian Angular Field

  38. 22nd IEEE International Workshop on High Performance Computational Biology (HiCOMB 2023)

  39. Unsupervised structural classification of dissolved organic matter based on fragmentation pathways

  40. Systems and methods for peptide identification

  41. Systems and methods for measuring similarity between mass spectra and peptides

  42. Systems And Methods For Diagnosing Autism Spectrum Disorder Using fMRI Data

  43. SPERTL: Epileptic Seizure Prediction using EEG with ResNets and Transfer Learning

  44. Re-configurable Hardware for Computational Proteomics

  45. Need for High-Performance Computing for MS-Based Omics Data Analysis

  46. Molecular level characterization of DOM along a freshwater-to-estuarine coastal gradient in the Florida Everglades

  47. Machine-Learning and the Future of HPC for MS-Based Omics

  48. Introduction to Mass Spectrometry Data

  49. High-Performance Computing Strategy Using Distributed-Memory Supercomputers

  50. High-Performance Algorithms for Mass Spectrometry-Based Omics

  51. G-MSR: A GPU-Based Dimensionality Reduction Algorithm

  52. Fast Spectral Pre-processing for Big MS Data

  53. Existing HPC Methods and the Communication Lower Bounds for Distributed-Memory Computations for Mass Spectrometry-Based Omics Data

  54. Computational CPU-GPU Template for Pre-processing of Floating-Point MS Data

  55. Communication lower-bounds for distributed-memory computations for mass spectrometry based omics data

  56. Classification of Autism Spectrum Disorder Using rs-fMRI data and Graph Convolutional Networks

  57. Biomedical IoT: Enabling Technologies, Architectural Elements, Challenges, and Future Directions

  58. A Easy to Use Generalized Template to Support Development of GPU Algorithms

  59. TurboBFS: GPU Based Breadth-First Search (BFS) Algorithms in the Language of Linear Algebra

  60. TurboBC: A Memory Efficient and Scalable GPU Based Betweenness Centrality Algorithm in the Language of Linear Algebra

  61. SpeCollate: Deep cross-modal similarity network for mass spectrometry data based peptide deductions

  62. Source data: high performance computing framework for tera-scale database search of mass spectrometry data

  63. Simulation Testbed for Evaluating Distributed Querying and Searching of Mass Spectrometry Big Data in a Network-based Infrastructure

  64. Search feasibility in distributed MS-proteomics big data

  65. Real-time peptide identification from high-throughput mass-spectrometry data

  66. Methods for Proteogenomics Data Analysis, Challenges, and Scalability Bottlenecks: A Survey

  67. Machine Learning methods for diagnosing Autism Spectrum Disorder and Attention-deficit/Hyperactivity Disorder using functional and structural MRI: A Survey

  68. High performance computing framework for tera-scale database search of mass spectrometry data

  69. HiCOPS: High Performance Computing Framework for Tera-Scale Database Search of Mass Spectrometry based Omics Data

  70. Graph Theoretic Approach for the Analysis of Comprehensive Mass-Spectrometry (MS/MS) Data of Dissolved Organic Matter

  71. Explainable and scalable machine learning algorithms for detection of autism spectrum disorder using fMRI data

  72. DeepCOVIDNet: Deep Convolutional Neural Network for COVID-19 Detection from Chest Radiographic Images

  73. Communication-avoiding micro-architecture to compute Xcorr scores for peptide identification

  74. Benchmarking mass spectrometry based proteomics algorithms using a simulated database

  75. ASD-SAENet: a sparse autoencoder, and deep-neural network model for detecting autism spectrum disorder (ASD) using fMRI data

  76. A Multi-Factorial Assessment of Functional Human Autistic Spectrum Brain Network Analysis

  77. ASD-DiagNet: A Hybrid Learning Approach for Detection of Autism Spectrum Disorder Using fMRI Data

  78. NGS-Integrator: An efficient tool for combining multiple NGS data tracks using minimum Bayes’ factors

  79. Methods and systems for compressing data

  80. Federated learning: A survey on enabling technologies, protocols, and applications

  81. Slm-transform: A method for memory-efficient indexing of spectra for database search in lc-ms/ms proteomics

  82. Optimized CNN-based diagnosis system to detect the pneumonia from chest radiographs

  83. NGS‐Integrator: A Tool for Combining Information from Multiple Genome‐Wide NGS Data Tracks Using Minimum Bayes Factors

  84. LBE: A Computational Load Balancing Algorithm for Speeding up Parallel Peptide Search in Mass-Spectrometry based Proteomics

  85. GPU-SFFT: A GPU based parallel algorithm for computing the Sparse Fast Fourier Transform (SFFT) of k-sparse signals

  86. GPU-DFC: A GPU-based parallel algorithm for computing dynamic-functional connectivity of big fMRI data

  87. Efficient shared peak counting in database peptide search using compact data structure for fragment-ion index

  88. Auto-ASD-Network: A technique based on Deep Learning and Support Vector Machines for diagnosing Autism Spectrum Disorder using fMRI data

  89. ASD-DiagNet: A hybrid learning approach for detection of Autism Spectrum Disorder using fMRI data

  90. 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

  91. Towards quantifying psychiatric diagnosis using machine learning algorithms and big fMRI data

  92. Similarity based classification of ADHD using Singular Value Decomposition

  93. Parallel sampling-pipeline for indefinite stream of heterogeneous graphs using OpenCL for FPGAs

  94. MaSS‐Simulator: A Highly Configurable Simulator for Generating MS/MS Datasets for Benchmarking of Proteomics Algorithms

  95. GPU-DAEMON: GPU algorithm design, data management & optimization template for array based big omics data

  96. Fast-GPU-PCC: A GPU-Based Technique to Compute Pairwise Pearson’s Correlation Coefficients for Time Series Data - An fMRI Study

  97. A Fourier-Based Data Minimization Algorithm for Fast and Secure Transfer of Big Genomic Datasets

  98. A deep learning-based data minimization algorithm for fast and secure transfer of big genomic datasets

  99. Scalable data structure to compress next-generation sequencing files and its application to compressive genomics

  100. Power-Efficient and Highly Scalable Parallel Graph Sampling using FPGAs

  101. GPU-PCC: A GPU Based Technique to Compute Pairwise Pearson's Correlation Coefficients for Big fMRI Data

  102. An out-of-core gpu based dimensionality reduction algorithm for big mass spectrometry data and its application in bottom-up proteomics

  103. A new cryptography algorithm to protect cloud-based healthcare services

  104. A Hybrid MPI-OpenMP Strategy to Speedup the Compression of Big Next-Generation Sequencing Datasets

  105. Systems-level analysis reveals selective regulation of Aqp2 gene expression by vasopressin

  106. Reductive Analytics on Big MS Data leads to tremendous reduction in time for peptide deduction

  107. MS-REDUCE: an ultrafast technique for reduction of big mass spectrometry data for high-throughput processing

  108. Introduction to the selected papers from the 7th International Conference on Bioinformatics and Computational Biology (BICoB 2015)

  109. GPU-ArraySort: A parallel, in-place algorithm for sorting large number of arrays

  110. Data Aware Communication for Energy Harvesting Sensor Networks

  111. A variable-length network encoding protocol for big genomic data

  112. A Parallel Peptide Indexer and Decoy Generator for Crux Tide using OpenMP

  113. On the sampling of big mass spectrometry data

  114. Design and implementation of network transfer protocol for big genomic data

  115. Big data proteogenomics and high performance computing: Challenges and opportunities

  116. Autophagic degradation of aquaporin-2 is an early event in hypokalemia-induced nephrogenic diabetes insipidus

  117. A parallel algorithm for compression of big next-generation sequencing datasets

  118. Global analysis of the effects of the V2 receptor antagonist satavaptan on protein phosphorylation in collecting duct

  119. Foreword to the special issue on selected papers from the 6th International Conference on Bioinformatics and Computational Biology (BICoB 2014).

  120. Exploiting thread-level and instruction-level parallelism to cluster mass spectrometry data using multicore architectures

  121. Cams-rs: clustering algorithm for large-scale mass spectrometry data using restricted search space and intelligent random sampling

  122. A knowledge base of vasopressin actions in the kidney

  123. 6th International Conference on Bioinformatics and Computational Biology (BICoB 2014)

  124. Quantitative phosphoproteomics implicates clusters of proteins involved in cell‐cell adhesion and transcriptional regulation in the vasopressin signaling network

  125. Proteome-wide measurement of protein half-lives and translation rates in vasopressin-sensitive collecting duct cells

  126. PhosSA: Fast and accurate phosphorylation site assignment algorithm for mass spectrometry data

  127. Foreword to the special issue on selected papers from the 5th International Conference on Bioinformatics and Computational Biology (BICoB 2013)

  128. A high performance algorithm for clustering of large-scale protein mass spectrometry data using multi-core architectures

  129. A Graphical User Interface (GUI) for Phosphorylation Site Assignment of Protein Mass Spectrometry Data

  130. Quantitative phosphoproteomics in nuclei of vasopressin-sensitive renal collecting duct cells

  131. Proteomic and Metabolomic Approaches to Cell Physiology and Pathophysiology: Quantitative phosphoproteomics in nuclei of vasopressin-sensitive renal collecting duct cells

  132. NHLBI-AbDesigner: an online tool for design of peptide-directed antibodies

  133. Identifying protein kinase target preferences using mass spectrometry

  134. High performance phosphorylation site assignment algorithm for mass spectrometry data using multicore systems

  135. Dynamics of the G protein-coupled vasopressin V2 receptor signaling network revealed by quantitative phosphoproteomics

  136. CP hos: a program to calculate and visualize evolutionarily conserved functional phosphorylation sites

  137. An efficient dynamic programming algorithm for phosphorylation site assignment of large-scale mass spectrometry data

  138. An efficient algorithm for clustering of large-scale mass spectrometry data

  139. A high performance multiple sequence alignment system for pyrosequencing reads from multiple reference genomes

  140. Mining temporal patterns from iTRAQ mass spectrometry (LC-MS/MS) data

  141. Mapping‐based temporal pattern mining algorithm (MTPMA) identifies unique clusters of phosphopeptides regulated by vasopressin in collecting duct

  142. Large‐scale iTRAQ‐based quantification of phosphorylation changes during vasopressin signaling

  143. Parallel Algorithm for Center Star Sequence and Alignments with Applications to Short Reads

  144. High performance computational biology algorithms

  145. A graph-theoretic framework for efficient computation of HMM based motif finder

  146. Pyro-align: Sample-align based multiple alignment system for pyrosequencing reads of large number

  147. Multiple sequence alignment system for pyrosequencing reads

  148. An Overview of Multiple Sequence Alignment Systems

  149. A domain decomposition strategy for alignment of multiple biological sequences on multiprocessor platforms

  150. Sample-align-d: A high performance multiple sequence alignment system using phylogenetic sampling and domain decomposition

Posts

NIH Funding Mechanisms (Research and Development) - part 1

Informal blog for better scientific communication